Data Source and Introduction

Name:

Don’t Get Kicked!!

From:

Kaggle Competition

Introduction:

This data set was published by Carvana at www.kaggle.com, which refers to the considerations a buyer has to make in order to buy a used car that’s in the best possible conditions.

Source:

Carvana

URL:

https://www.kaggle.com/c/DontGetKicked/data.

##Variable Description

Independent Variables

kable(vars[-4, ], table.attr='class="table-fixed-header"', row.names = F) %>%
  kable_styling(full_width=F)
Field.Name Variable.Type Definition
PurchDate Continuous The Date the vehicle was Purchased at Auction
VehicleAge Continuous The Years elapsed since the manufacturer’s year
VehOdo Continuous The vehicles odometer reading
MMRAcquisitionAuctionCleanPrice Continuous Acquisition price for this vehicle in the above Average condition at time of purchase
MMRAcquisitionRetailAveragePrice Continuous Acquisition price for this vehicle in the retail market in average condition at time of purchase
MMRAcquisitionRetailCleanPrice Continuous Acquisition price for this vehicle in the retail market in above average condition at time of purchase
MMRCurrentAuctionAveragePrice Continuous Acquisition price for this vehicle in average condition as of current day
MMRCurrentAuctionCleanPrice Continuous Acquisition price for this vehicle in the above condition as of current day
MMRCurrentRetailAveragePrice Continuous Acquisition price for this vehicle in the retail market in average condition as of current day
MMRCurrentRetailCleanPrice Continuous Acquisition price for this vehicle in the retail market in above average condition as of current day
VNZIP Continuous Zipcode where the car was purchased
VehBCost Continuous Acquisition cost paid for the vehicle at time of purchase
WarrantyCost Continuous Warranty price (term=36month and millage=36K)
RefID Category Unique (sequential) number assigned to vehicles
IsBadBuy Category Identifies if the kicked vehicle was an avoidable purchase
Auction Category Auction provider at which the vehicle was purchased
VehYear Category The manufacturer’s year of the vehicle
Make Category Vehicle Manufacturer
Model Category Vehicle Model
Trim Category Vehicle Trim Level
SubModel Category Vehicle Submodel
Color Category Vehicle Color
Transmission Category Vehicles transmission type (Automatic, Manual)
WheelTypeID Category The type id of the vehicle wheel
WheelType Category The vehicle wheel type description (Alloy, Covers)
Nationality Category The Manufacturer’s country
Size Category The size category of the vehicle (Compact, SUV, etc.)
TopThreeAmericanName Category Identifies if the manufacturer is one of the top three American manufacturers
PRIMEUNIT Category Identifies if the vehicle would have a higher demand than a standard purchase
AUCGUART Category The level guarntee provided by auction for the vehicle (Green light - Guaranteed/arbitratable, Yellow Light - caution/issue, red light - sold as is)
BYRNO Category Unique number assigned to the buyer that purchased the vehicle
VNST Category State where the the car was purchased
IsOnlineSale Category Identifies if the vehicle was originally purchased online

Dependent Variables

kable(vars[4, ], table.attr='class="table-fixed-header"', row.names=F) %>%
  kable_styling(position="left", full_width=T)
Field.Name Variable.Type Definition
MMRAcquisitionAuctionAveragePrice Continuous Acquisition price for this vehicle in average condition at time of purchase

Data Appearance

kable(head(cars), table.attr='class="table-fixed-header"') %>%
  kable_styling() %>%
  scroll_box(height=F)
RefId IsBadBuy PurchDate Auction VehYear VehicleAge Make Model Trim SubModel Color Transmission WheelTypeID WheelType VehOdo Nationality Size TopThreeAmericanName MMRAcquisitionAuctionAveragePrice MMRAcquisitionAuctionCleanPrice MMRAcquisitionRetailAveragePrice MMRAcquisitionRetailCleanPrice MMRCurrentAuctionAveragePrice MMRCurrentAuctionCleanPrice MMRCurrentRetailAveragePrice MMRCurrentRetailCleanPrice PRIMEUNIT AUCGUART BYRNO VNZIP VNST VehBCost IsOnlineSale WarrantyCost
1 0 12/7/2009 ADESA 2006 3 MAZDA MAZDA3 i 4D SEDAN I RED AUTO 1 Alloy 89046 OTHER ASIAN MEDIUM OTHER 8155 9829 11636 13600 7451 8552 11597 12409 NULL NULL 21973 33619 FL 7100 0 1113
2 0 12/7/2009 ADESA 2004 5 DODGE 1500 RAM PICKUP 2WD ST QUAD CAB 4.7L SLT WHITE AUTO 1 Alloy 93593 AMERICAN LARGE TRUCK CHRYSLER 6854 8383 10897 12572 7456 9222 11374 12791 NULL NULL 19638 33619 FL 7600 0 1053
3 0 12/7/2009 ADESA 2005 4 DODGE STRATUS V6 SXT 4D SEDAN SXT FFV MAROON AUTO 2 Covers 73807 AMERICAN MEDIUM CHRYSLER 3202 4760 6943 8457 4035 5557 7146 8702 NULL NULL 19638 33619 FL 4900 0 1389
4 0 12/7/2009 ADESA 2004 5 DODGE NEON SXT 4D SEDAN SILVER AUTO 1 Alloy 65617 AMERICAN COMPACT CHRYSLER 1893 2675 4658 5690 1844 2646 4375 5518 NULL NULL 19638 33619 FL 4100 0 630
5 0 12/7/2009 ADESA 2005 4 FORD FOCUS ZX3 2D COUPE ZX3 SILVER MANUAL 2 Covers 69367 AMERICAN COMPACT FORD 3913 5054 7723 8707 3247 4384 6739 7911 NULL NULL 19638 33619 FL 4000 0 1020
6 0 12/7/2009 ADESA 2004 5 MITSUBISHI GALANT 4C ES 4D SEDAN ES WHITE AUTO 2 Covers 81054 OTHER ASIAN MEDIUM OTHER 3901 4908 6706 8577 4709 5827 8149 9451 NULL NULL 19638 33619 FL 5600 0 594

Summary of Variable

Dependent Variable

dep <- as.data.frame(table(IsBadBuy))
kable(dep, table.attr='class="table-fixed-header"', align="l") %>%
  kable_styling(full_width=F, position="float_left")
IsBadBuy Freq
0 64007
1 8976

Tables of Categorical Variables

Categorical

0
(n=64007)
1
(n=8976)
Total
(n=72983)
Auction
ADESA 12246 (19.1%) 2193 (24.4%) 14439 (19.8%)
MANHEIM 36328 (56.8%) 4715 (52.5%) 41043 (56.2%)
OTHER 15433 (24.1%) 2068 (23.0%) 17501 (24.0%)
VehYear
2001 1055 (1.6%) 426 (4.7%) 1481 (2.0%)
2002 2587 (4.0%) 818 (9.1%) 3405 (4.7%)
2003 5017 (7.8%) 1210 (13.5%) 6227 (8.5%)
2004 8620 (13.5%) 1587 (17.7%) 10207 (14.0%)
2005 13457 (21.0%) 2032 (22.6%) 15489 (21.2%)
2006 15443 (24.1%) 1600 (17.8%) 17043 (23.4%)
2007 10535 (16.5%) 888 (9.9%) 11423 (15.7%)
2008 6500 (10.2%) 385 (4.3%) 6885 (9.4%)
2009 792 (1.2%) 30 (0.3%) 822 (1.1%)
2010 1 (0.0%) 0 (0%) 1 (0.0%)
VehicleAge
0 2 (0.0%) 0 (0%) 2 (0.0%)
1 2969 (4.6%) 125 (1.4%) 3094 (4.2%)
2 7942 (12.4%) 540 (6.0%) 8482 (11.6%)
3 14601 (22.8%) 1301 (14.5%) 15902 (21.8%)
4 15149 (23.7%) 1864 (20.8%) 17013 (23.3%)
5 11061 (17.3%) 1895 (21.1%) 12956 (17.8%)
6 6575 (10.3%) 1447 (16.1%) 8022 (11.0%)
7 3641 (5.7%) 1005 (11.2%) 4646 (6.4%)
8 1623 (2.5%) 597 (6.7%) 2220 (3.0%)
9 444 (0.7%) 202 (2.3%) 646 (0.9%)
Make
ACURA 24 (0.0%) 9 (0.1%) 33 (0.0%)
BUICK 607 (0.9%) 113 (1.3%) 720 (1.0%)
CADILLAC 28 (0.0%) 5 (0.1%) 33 (0.0%)
CHEVROLET 15567 (24.3%) 1681 (18.7%) 17248 (23.6%)
CHRYSLER 7707 (12.0%) 1137 (12.7%) 8844 (12.1%)
DODGE 11579 (18.1%) 1333 (14.9%) 12912 (17.7%)
FORD 9563 (14.9%) 1742 (19.4%) 11305 (15.5%)
GMC 574 (0.9%) 75 (0.8%) 649 (0.9%)
HONDA 443 (0.7%) 54 (0.6%) 497 (0.7%)
HUMMER 1 (0.0%) 0 (0%) 1 (0.0%)
HYUNDAI 1578 (2.5%) 233 (2.6%) 1811 (2.5%)
INFINITI 28 (0.0%) 14 (0.2%) 42 (0.1%)
ISUZU 125 (0.2%) 9 (0.1%) 134 (0.2%)
JEEP 1390 (2.2%) 254 (2.8%) 1644 (2.3%)
KIA 2192 (3.4%) 292 (3.3%) 2484 (3.4%)
LEXUS 20 (0.0%) 11 (0.1%) 31 (0.0%)
LINCOLN 68 (0.1%) 29 (0.3%) 97 (0.1%)
MAZDA 821 (1.3%) 158 (1.8%) 979 (1.3%)
MERCURY 758 (1.2%) 155 (1.7%) 913 (1.3%)
MINI 16 (0.0%) 8 (0.1%) 24 (0.0%)
MITSUBISHI 907 (1.4%) 123 (1.4%) 1030 (1.4%)
NISSAN 1752 (2.7%) 333 (3.7%) 2085 (2.9%)
OLDSMOBILE 194 (0.3%) 49 (0.5%) 243 (0.3%)
PLYMOUTH 1 (0.0%) 1 (0.0%) 2 (0.0%)
PONTIAC 3751 (5.9%) 507 (5.6%) 4258 (5.8%)
SATURN 1857 (2.9%) 306 (3.4%) 2163 (3.0%)
SCION 118 (0.2%) 11 (0.1%) 129 (0.2%)
SUBARU 22 (0.0%) 6 (0.1%) 28 (0.0%)
SUZUKI 1133 (1.8%) 195 (2.2%) 1328 (1.8%)
TOYOTA 1030 (1.6%) 114 (1.3%) 1144 (1.6%)
TOYOTA SCION 1 (0.0%) 0 (0%) 1 (0.0%)
VOLKSWAGEN 115 (0.2%) 19 (0.2%) 134 (0.2%)
VOLVO 37 (0.1%) 0 (0%) 37 (0.1%)
Color
BEIGE 1373 (2.1%) 211 (2.4%) 1584 (2.2%)
BLACK 6769 (10.6%) 858 (9.6%) 7627 (10.5%)
BLUE 9158 (14.3%) 1189 (13.2%) 10347 (14.2%)
BROWN 380 (0.6%) 56 (0.6%) 436 (0.6%)
GOLD 4494 (7.0%) 737 (8.2%) 5231 (7.2%)
GREEN 2792 (4.4%) 402 (4.5%) 3194 (4.4%)
GREY 6976 (10.9%) 911 (10.1%) 7887 (10.8%)
MAROON 1786 (2.8%) 260 (2.9%) 2046 (2.8%)
NOT AVAIL 70 (0.1%) 24 (0.3%) 94 (0.1%)
NULL 7 (0.0%) 1 (0.0%) 8 (0.0%)
ORANGE 381 (0.6%) 34 (0.4%) 415 (0.6%)
OTHER 213 (0.3%) 29 (0.3%) 242 (0.3%)
PURPLE 317 (0.5%) 56 (0.6%) 373 (0.5%)
RED 5432 (8.5%) 825 (9.2%) 6257 (8.6%)
SILVER 13032 (20.4%) 1843 (20.5%) 14875 (20.4%)
WHITE 10617 (16.6%) 1506 (16.8%) 12123 (16.6%)
YELLOW 210 (0.3%) 34 (0.4%) 244 (0.3%)
Transmission
NULL 8 (0.0%) 1 (0.0%) 9 (0.0%)
AUTO 61722 (96.4%) 8676 (96.7%) 70398 (96.5%)
MANUAL 2277 (3.6%) 299 (3.3%) 2576 (3.5%)
WheelType
Alloy 32065 (50.1%) 3985 (44.4%) 36050 (49.4%)
Covers 30349 (47.4%) 2655 (29.6%) 33004 (45.2%)
NULL 937 (1.5%) 2237 (24.9%) 3174 (4.3%)
Special 656 (1.0%) 99 (1.1%) 755 (1.0%)
Nationality
AMERICAN 53641 (83.8%) 7387 (82.3%) 61028 (83.6%)
NULL 5 (0.0%) 0 (0%) 5 (0.0%)
OTHER 168 (0.3%) 27 (0.3%) 195 (0.3%)
OTHER ASIAN 6972 (10.9%) 1061 (11.8%) 8033 (11.0%)
TOP LINE ASIAN 3221 (5.0%) 501 (5.6%) 3722 (5.1%)
Size
COMPACT 6060 (9.5%) 1145 (12.8%) 7205 (9.9%)
CROSSOVER 1576 (2.5%) 183 (2.0%) 1759 (2.4%)
LARGE 8032 (12.5%) 818 (9.1%) 8850 (12.1%)
LARGE SUV 1201 (1.9%) 232 (2.6%) 1433 (2.0%)
LARGE TRUCK 2810 (4.4%) 360 (4.0%) 3170 (4.3%)
MEDIUM 27244 (42.6%) 3541 (39.4%) 30785 (42.2%)
MEDIUM SUV 6897 (10.8%) 1193 (13.3%) 8090 (11.1%)
NULL 5 (0.0%) 0 (0%) 5 (0.0%)
SMALL SUV 1963 (3.1%) 313 (3.5%) 2276 (3.1%)
SMALL TRUCK 739 (1.2%) 125 (1.4%) 864 (1.2%)
SPECIALTY 1739 (2.7%) 176 (2.0%) 1915 (2.6%)
SPORTS 633 (1.0%) 144 (1.6%) 777 (1.1%)
VAN 5108 (8.0%) 746 (8.3%) 5854 (8.0%)
TopThreeAmericanName
CHRYSLER 20674 (32.3%) 2725 (30.4%) 23399 (32.1%)
FORD 10389 (16.2%) 1926 (21.5%) 12315 (16.9%)
GM 22578 (35.3%) 2736 (30.5%) 25314 (34.7%)
NULL 5 (0.0%) 0 (0%) 5 (0.0%)
OTHER 10361 (16.2%) 1589 (17.7%) 11950 (16.4%)
PRIMEUNIT
NO 3230 (5.0%) 127 (1.4%) 3357 (4.6%)
NULL 60721 (94.9%) 8843 (98.5%) 69564 (95.3%)
YES 56 (0.1%) 6 (0.1%) 62 (0.1%)
AUCGUART
GREEN 3215 (5.0%) 125 (1.4%) 3340 (4.6%)
NULL 60721 (94.9%) 8843 (98.5%) 69564 (95.3%)
RED 71 (0.1%) 8 (0.1%) 79 (0.1%)
VNST
AL 602 (0.9%) 88 (1.0%) 690 (0.9%)
AR 54 (0.1%) 16 (0.2%) 70 (0.1%)
AZ 5470 (8.5%) 704 (7.8%) 6174 (8.5%)
CA 6144 (9.6%) 951 (10.6%) 7095 (9.7%)
CO 4394 (6.9%) 604 (6.7%) 4998 (6.8%)
FL 9305 (14.5%) 1142 (12.7%) 10447 (14.3%)
GA 2177 (3.4%) 273 (3.0%) 2450 (3.4%)
IA 426 (0.7%) 73 (0.8%) 499 (0.7%)
ID 177 (0.3%) 19 (0.2%) 196 (0.3%)
IL 391 (0.6%) 67 (0.7%) 458 (0.6%)
IN 418 (0.7%) 68 (0.8%) 486 (0.7%)
KY 215 (0.3%) 15 (0.2%) 230 (0.3%)
LA 297 (0.5%) 52 (0.6%) 349 (0.5%)
MA 13 (0.0%) 2 (0.0%) 15 (0.0%)
MD 993 (1.6%) 165 (1.8%) 1158 (1.6%)
MI 13 (0.0%) 1 (0.0%) 14 (0.0%)
MN 60 (0.1%) 2 (0.0%) 62 (0.1%)
MO 676 (1.1%) 82 (0.9%) 758 (1.0%)
MS 447 (0.7%) 46 (0.5%) 493 (0.7%)
NC 6243 (9.8%) 799 (8.9%) 7042 (9.6%)
NE 25 (0.0%) 1 (0.0%) 26 (0.0%)
NH 88 (0.1%) 9 (0.1%) 97 (0.1%)
NJ 277 (0.4%) 40 (0.4%) 317 (0.4%)
NM 210 (0.3%) 29 (0.3%) 239 (0.3%)
NV 472 (0.7%) 90 (1.0%) 562 (0.8%)
NY 6 (0.0%) 0 (0%) 6 (0.0%)
OH 728 (1.1%) 67 (0.7%) 795 (1.1%)
OK 3263 (5.1%) 331 (3.7%) 3594 (4.9%)
OR 198 (0.3%) 13 (0.1%) 211 (0.3%)
PA 700 (1.1%) 147 (1.6%) 847 (1.2%)
SC 3686 (5.8%) 594 (6.6%) 4280 (5.9%)
TN 1561 (2.4%) 203 (2.3%) 1764 (2.4%)
TX 11719 (18.3%) 1877 (20.9%) 13596 (18.6%)
UT 769 (1.2%) 106 (1.2%) 875 (1.2%)
VA 1399 (2.2%) 263 (2.9%) 1662 (2.3%)
WA 128 (0.2%) 8 (0.1%) 136 (0.2%)
WV 263 (0.4%) 29 (0.3%) 292 (0.4%)
IsOnlineSale
0 62375 (97.5%) 8763 (97.6%) 71138 (97.5%)
1 1632 (2.5%) 213 (2.4%) 1845 (2.5%)
VNZIP
2764 13 (0.0%) 2 (0.0%) 15 (0.0%)
3106 88 (0.1%) 9 (0.1%) 97 (0.1%)
8505 277 (0.4%) 40 (0.4%) 317 (0.4%)
12552 6 (0.0%) 0 (0%) 6 (0.0%)
16066 6 (0.0%) 0 (0%) 6 (0.0%)
16137 2 (0.0%) 0 (0%) 2 (0.0%)
17028 33 (0.1%) 0 (0%) 33 (0.0%)
17406 119 (0.2%) 20 (0.2%) 139 (0.2%)
17545 100 (0.2%) 36 (0.4%) 136 (0.2%)
19440 440 (0.7%) 91 (1.0%) 531 (0.7%)
20166 458 (0.7%) 111 (1.2%) 569 (0.8%)
21014 179 (0.3%) 4 (0.0%) 183 (0.3%)
21075 814 (1.3%) 161 (1.8%) 975 (1.3%)
22403 437 (0.7%) 85 (0.9%) 522 (0.7%)
22801 467 (0.7%) 66 (0.7%) 533 (0.7%)
23234 4 (0.0%) 1 (0.0%) 5 (0.0%)
23606 33 (0.1%) 0 (0%) 33 (0.0%)
25071 0 (0%) 1 (0.0%) 1 (0.0%)
25177 142 (0.2%) 13 (0.1%) 155 (0.2%)
26431 121 (0.2%) 15 (0.2%) 136 (0.2%)
27407 483 (0.8%) 91 (1.0%) 574 (0.8%)
27542 3072 (4.8%) 330 (3.7%) 3402 (4.7%)
28273 1606 (2.5%) 281 (3.1%) 1887 (2.6%)
28625 1082 (1.7%) 97 (1.1%) 1179 (1.6%)
29070 247 (0.4%) 18 (0.2%) 265 (0.4%)
29323 3 (0.0%) 0 (0%) 3 (0.0%)
29461 285 (0.4%) 53 (0.6%) 338 (0.5%)
29532 1472 (2.3%) 203 (2.3%) 1675 (2.3%)
29697 1679 (2.6%) 320 (3.6%) 1999 (2.7%)
30120 115 (0.2%) 39 (0.4%) 154 (0.2%)
30212 526 (0.8%) 69 (0.8%) 595 (0.8%)
30272 1043 (1.6%) 120 (1.3%) 1163 (1.6%)
30315 120 (0.2%) 9 (0.1%) 129 (0.2%)
30331 372 (0.6%) 34 (0.4%) 406 (0.6%)
30529 1 (0.0%) 2 (0.0%) 3 (0.0%)
32124 1045 (1.6%) 117 (1.3%) 1162 (1.6%)
32219 590 (0.9%) 104 (1.2%) 694 (1.0%)
32225 5 (0.0%) 0 (0%) 5 (0.0%)
32503 29 (0.0%) 5 (0.1%) 34 (0.0%)
32750 208 (0.3%) 59 (0.7%) 267 (0.4%)
32772 69 (0.1%) 16 (0.2%) 85 (0.1%)
32812 73 (0.1%) 0 (0%) 73 (0.1%)
32824 3352 (5.2%) 347 (3.9%) 3699 (5.1%)
33073 107 (0.2%) 0 (0%) 107 (0.1%)
33311 114 (0.2%) 22 (0.2%) 136 (0.2%)
33314 161 (0.3%) 24 (0.3%) 185 (0.3%)
33411 237 (0.4%) 36 (0.4%) 273 (0.4%)
33619 1558 (2.4%) 181 (2.0%) 1739 (2.4%)
33762 148 (0.2%) 28 (0.3%) 176 (0.2%)
33809 608 (0.9%) 64 (0.7%) 672 (0.9%)
33916 26 (0.0%) 8 (0.1%) 34 (0.0%)
34203 163 (0.3%) 41 (0.5%) 204 (0.3%)
34761 812 (1.3%) 90 (1.0%) 902 (1.2%)
35004 414 (0.6%) 64 (0.7%) 478 (0.7%)
35613 188 (0.3%) 24 (0.3%) 212 (0.3%)
37122 253 (0.4%) 13 (0.1%) 266 (0.4%)
37138 17 (0.0%) 1 (0.0%) 18 (0.0%)
37210 488 (0.8%) 49 (0.5%) 537 (0.7%)
37421 245 (0.4%) 3 (0.0%) 248 (0.3%)
37771 313 (0.5%) 98 (1.1%) 411 (0.6%)
38118 230 (0.4%) 39 (0.4%) 269 (0.4%)
38128 15 (0.0%) 0 (0%) 15 (0.0%)
38637 199 (0.3%) 25 (0.3%) 224 (0.3%)
39208 131 (0.2%) 5 (0.1%) 136 (0.2%)
39402 117 (0.2%) 16 (0.2%) 133 (0.2%)
42104 215 (0.3%) 15 (0.2%) 230 (0.3%)
43207 11 (0.0%) 1 (0.0%) 12 (0.0%)
45005 696 (1.1%) 62 (0.7%) 758 (1.0%)
45011 21 (0.0%) 4 (0.0%) 25 (0.0%)
46239 88 (0.1%) 34 (0.4%) 122 (0.2%)
46803 122 (0.2%) 8 (0.1%) 130 (0.2%)
47129 208 (0.3%) 26 (0.3%) 234 (0.3%)
48265 13 (0.0%) 1 (0.0%) 14 (0.0%)
50111 426 (0.7%) 73 (0.8%) 499 (0.7%)
55369 60 (0.1%) 2 (0.0%) 62 (0.1%)
60440 159 (0.2%) 16 (0.2%) 175 (0.2%)
60443 103 (0.2%) 14 (0.2%) 117 (0.2%)
60445 87 (0.1%) 31 (0.3%) 118 (0.2%)
62207 42 (0.1%) 6 (0.1%) 48 (0.1%)
63044 84 (0.1%) 12 (0.1%) 96 (0.1%)
64153 9 (0.0%) 1 (0.0%) 10 (0.0%)
64161 583 (0.9%) 69 (0.8%) 652 (0.9%)
68138 25 (0.0%) 1 (0.0%) 26 (0.0%)
70002 13 (0.0%) 0 (0%) 13 (0.0%)
70401 91 (0.1%) 16 (0.2%) 107 (0.1%)
70460 91 (0.1%) 10 (0.1%) 101 (0.1%)
71119 102 (0.2%) 26 (0.3%) 128 (0.2%)
72117 54 (0.1%) 16 (0.2%) 70 (0.1%)
73108 1045 (1.6%) 182 (2.0%) 1227 (1.7%)
73129 36 (0.1%) 10 (0.1%) 46 (0.1%)
74135 2182 (3.4%) 139 (1.5%) 2321 (3.2%)
75020 246 (0.4%) 39 (0.4%) 285 (0.4%)
75050 1372 (2.1%) 282 (3.1%) 1654 (2.3%)
75061 330 (0.5%) 55 (0.6%) 385 (0.5%)
75236 2095 (3.3%) 336 (3.7%) 2431 (3.3%)
76040 1404 (2.2%) 201 (2.2%) 1605 (2.2%)
76063 324 (0.5%) 21 (0.2%) 345 (0.5%)
76101 1 (0.0%) 0 (0%) 1 (0.0%)
77041 1252 (2.0%) 159 (1.8%) 1411 (1.9%)
77061 500 (0.8%) 29 (0.3%) 529 (0.7%)
77073 111 (0.2%) 25 (0.3%) 136 (0.2%)
77086 781 (1.2%) 150 (1.7%) 931 (1.3%)
77301 8 (0.0%) 1 (0.0%) 9 (0.0%)
78219 1036 (1.6%) 137 (1.5%) 1173 (1.6%)
78227 753 (1.2%) 163 (1.8%) 916 (1.3%)
78426 55 (0.1%) 7 (0.1%) 62 (0.1%)
78610 72 (0.1%) 12 (0.1%) 84 (0.1%)
78745 77 (0.1%) 4 (0.0%) 81 (0.1%)
78754 1132 (1.8%) 220 (2.5%) 1352 (1.9%)
79605 104 (0.2%) 33 (0.4%) 137 (0.2%)
79932 66 (0.1%) 3 (0.0%) 69 (0.1%)
80011 1154 (1.8%) 98 (1.1%) 1252 (1.7%)
80022 1864 (2.9%) 254 (2.8%) 2118 (2.9%)
80112 1 (0.0%) 0 (0%) 1 (0.0%)
80229 596 (0.9%) 120 (1.3%) 716 (1.0%)
80817 779 (1.2%) 132 (1.5%) 911 (1.2%)
83687 14 (0.0%) 0 (0%) 14 (0.0%)
83716 163 (0.3%) 19 (0.2%) 182 (0.2%)
84087 154 (0.2%) 11 (0.1%) 165 (0.2%)
84104 615 (1.0%) 95 (1.1%) 710 (1.0%)
85009 712 (1.1%) 128 (1.4%) 840 (1.2%)
85018 146 (0.2%) 4 (0.0%) 150 (0.2%)
85040 1829 (2.9%) 183 (2.0%) 2012 (2.8%)
85204 32 (0.0%) 5 (0.1%) 37 (0.1%)
85226 1777 (2.8%) 309 (3.4%) 2086 (2.9%)
85248 1 (0.0%) 0 (0%) 1 (0.0%)
85260 0 (0%) 1 (0.0%) 1 (0.0%)
85284 508 (0.8%) 14 (0.2%) 522 (0.7%)
85338 1 (0.0%) 0 (0%) 1 (0.0%)
85353 464 (0.7%) 60 (0.7%) 524 (0.7%)
87105 208 (0.3%) 29 (0.3%) 237 (0.3%)
87109 2 (0.0%) 0 (0%) 2 (0.0%)
89120 105 (0.2%) 23 (0.3%) 128 (0.2%)
89139 7 (0.0%) 0 (0%) 7 (0.0%)
89165 353 (0.6%) 67 (0.7%) 420 (0.6%)
89506 7 (0.0%) 0 (0%) 7 (0.0%)
90045 234 (0.4%) 38 (0.4%) 272 (0.4%)
90650 148 (0.2%) 6 (0.1%) 154 (0.2%)
91752 970 (1.5%) 205 (2.3%) 1175 (1.6%)
91763 32 (0.0%) 3 (0.0%) 35 (0.0%)
91770 86 (0.1%) 7 (0.1%) 93 (0.1%)
92057 265 (0.4%) 47 (0.5%) 312 (0.4%)
92101 50 (0.1%) 0 (0%) 50 (0.1%)
92337 635 (1.0%) 176 (2.0%) 811 (1.1%)
92504 348 (0.5%) 37 (0.4%) 385 (0.5%)
92807 922 (1.4%) 164 (1.8%) 1086 (1.5%)
94544 696 (1.1%) 56 (0.6%) 752 (1.0%)
95673 1758 (2.7%) 212 (2.4%) 1970 (2.7%)
97060 63 (0.1%) 1 (0.0%) 64 (0.1%)
97217 124 (0.2%) 12 (0.1%) 136 (0.2%)
97402 11 (0.0%) 0 (0%) 11 (0.0%)
98064 123 (0.2%) 8 (0.1%) 131 (0.2%)
99224 5 (0.0%) 0 (0%) 5 (0.0%)

Continous

0
(n=64007)
1
(n=8976)
Overall
(n=72983)
VehOdo
Mean (SD) 71000 (14600) 74700 (14200) 71500 (14600)
Median [Min, Max] 72900 [5370, 114000] 76500 [4830, 116000] 73400 [4830, 116000]
MMRAcquisitionAuctionAveragePrice
Mean (SD) 6230 (2420) 5410 (2650) 6130 (2460)
Median [Min, Max] 6240 [0.00, 20300] 5010 [0.00, 35700] 6100 [0.00, 35700]
Missing 17 (0.0%) 1 (0.0%) 18 (0.0%)
MMRAcquisitionAuctionCleanPrice
Mean (SD) 7480 (2670) 6630 (2940) 7370 (2720)
Median [Min, Max] 7450 [0.00, 24000] 6200 [0.00, 36900] 7300 [0.00, 36900]
Missing 17 (0.0%) 1 (0.0%) 18 (0.0%)
MMRAcquisitionRetailAveragePrice
Mean (SD) 8600 (3110) 7760 (3350) 8500 (3160)
Median [Min, Max] 8570 [0.00, 24700] 7450 [0.00, 39100] 8440 [0.00, 39100]
Missing 17 (0.0%) 1 (0.0%) 18 (0.0%)
MMRAcquisitionRetailCleanPrice
Mean (SD) 9960 (3340) 9090 (3620) 9850 (3390)
Median [Min, Max] 9920 [0.00, 27500] 8830 [0.00, 41500] 9790 [0.00, 41500]
Missing 17 (0.0%) 1 (0.0%) 18 (0.0%)
MMRCurrentAuctionAveragePrice
Mean (SD) 6230 (2390) 5420 (2590) 6130 (2430)
Median [Min, Max] 6210 [0.00, 21800] 5020 [0.00, 35700] 6060 [0.00, 35700]
Missing 283 (0.4%) 32 (0.4%) 315 (0.4%)
MMRCurrentAuctionCleanPrice
Mean (SD) 7500 (2640) 6640 (2880) 7390 (2690)
Median [Min, Max] 7450 [0.00, 25800] 6210 [0.00, 36900] 7310 [0.00, 36900]
Missing 283 (0.4%) 32 (0.4%) 315 (0.4%)
MMRCurrentRetailAveragePrice
Mean (SD) 8900 (3050) 7920 (3270) 8780 (3090)
Median [Min, Max] 8890 [0.00, 24100] 7620 [0.00, 39100] 8730 [0.00, 39100]
Missing 283 (0.4%) 32 (0.4%) 315 (0.4%)
MMRCurrentRetailCleanPrice
Mean (SD) 10300 (3260) 9260 (3520) 10100 (3310)
Median [Min, Max] 10300 [0.00, 28400] 8980 [0.00, 41100] 10100 [0.00, 41100]
Missing 283 (0.4%) 32 (0.4%) 315 (0.4%)
VehBCost
Mean (SD) 6800 (1710) 6260 (2080) 6730 (1770)
Median [Min, Max] 6800 [1400, 16300] 6000 [1.00, 45500] 6700 [1.00, 45500]
WarrantyCost
Mean (SD) 1260 (586) 1360 (680) 1280 (599)
Median [Min, Max] 1160 [462, 7500] 1240 [462, 6490] 1160 [462, 7500]

Plots of Categorical Variables

Make

## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.
## Warning in L$marker$color[idx] <- aes2plotly(data, params, "fill")[idx]: 被
## 替換的項目不是替換值長度的倍數

Color

## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.
## Warning in L$marker$color[idx] <- aes2plotly(data, params, "fill")[idx]: 被
## 替換的項目不是替換值長度的倍數

Size

## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.
## Warning in L$marker$color[idx] <- aes2plotly(data, params, "fill")[idx]: 被
## 替換的項目不是替換值長度的倍數

VNST

## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.
## Warning in L$marker$color[idx] <- aes2plotly(data, params, "fill")[idx]: 被
## 替換的項目不是替換值長度的倍數

VNZIP

## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.
## Warning in L$marker$color[idx] <- aes2plotly(data, params, "fill")[idx]: 被
## 替換的項目不是替換值長度的倍數

Auction

VehYear

VehicleAge

Transmission

WheelType

Nationality

TopThreeAmericanName

PRIMEUNIT

AUCGUART

IsOnlineSale

#Plots of Continuous Variables{.tabset .tabset-dropdown}

VehOdo

## Coordinate system already present. Adding new coordinate system, which will replace the existing one.

VehBCost

## Coordinate system already present. Adding new coordinate system, which will replace the existing one.

WarrantyCost

## Coordinate system already present. Adding new coordinate system, which will replace the existing one.

MMRAcquisitionAuctionAveragePrice

MMRAcquisitionAuctionCleanPrice

MMRAcquisitionRetailAveragePrice

MMRAcquisitionRetailCleanPrice

MMRCurrentAuctionAveragePrice

MMRCurrentAuctionCleanPrice

MMRCurrentRetailAveragePrice

MMRCurrentRetailCleanPrice